What is a non-starving policy in reinforcement learning?
A non-starving policy is a (behavior) policy that is theoretically guaranteed to visit each state and take all possible actions from each state an infinite number of times, so that to always update $Q(s, a)$, $\forall s, \forall a$, an infinite number of times. In the context of off-policy prediction, this criterion implies that any trajectory will have no zero probability under a behavior policy. As a consequence, the experience from the behavior policy sufficiently covers the possibilities of any target policy.

An example of a non-starving policy is the $\epsilon$-greedy policy, which, with $0 &lt; \epsilon \leq 1$ (which is usually a small number between $0$ and $1$) probability, takes a random action from a given state, and, with $1-\epsilon$ probability, takes the current best action, that is, the action with the highest value from a given state, according to the current value function.